Physical Activity Recognition using Multiple Sensors Embedded in a Wearable Device
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nam, Yunyoung | - |
dc.contributor.author | Rho, Seungmin | - |
dc.contributor.author | Lee, Chulung | - |
dc.date.accessioned | 2021-09-06T04:44:18Z | - |
dc.date.available | 2021-09-06T04:44:18Z | - |
dc.date.created | 2021-06-14 | - |
dc.date.issued | 2013-02 | - |
dc.identifier.issn | 1539-9087 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/104066 | - |
dc.description.abstract | In this article, we present a wearable intelligence device for activity monitoring applications. We developed and evaluated algorithms to recognize physical activities from data acquired using a 3-axis accelerometer with a single camera worn on a body. The recognition process is performed in two steps: at first the features for defining a human activity are measured by the 3-axis accelerometer sensor and the image sensor embedded in a wearable device. Then, the physical activity corresponding to the measured features is determined by applying the SVM classifier. The 3-axis accelerometer sensor computes the correlation between axes and the magnitude of the FFT for other features of an activity. Acceleration data is classified into nine activity labels. Through the image sensor, multiple optical flow vectors computed on each grid image patch are extracted as features for defining an activity. In the experiments, we showed that an overall accuracy rate of activity recognition based our method was 92.78%. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ASSOC COMPUTING MACHINERY | - |
dc.title | Physical Activity Recognition using Multiple Sensors Embedded in a Wearable Device | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Chulung | - |
dc.identifier.doi | 10.1145/2423636.2423644 | - |
dc.identifier.scopusid | 2-s2.0-84874838130 | - |
dc.identifier.wosid | 000327432400008 | - |
dc.identifier.bibliographicCitation | ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, v.12, no.2 | - |
dc.relation.isPartOf | ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS | - |
dc.citation.title | ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS | - |
dc.citation.volume | 12 | - |
dc.citation.number | 2 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.subject.keywordAuthor | Reliability | - |
dc.subject.keywordAuthor | Algorithms | - |
dc.subject.keywordAuthor | Accelerometer | - |
dc.subject.keywordAuthor | human activity recognition | - |
dc.subject.keywordAuthor | SVM | - |
dc.subject.keywordAuthor | ubiquitous | - |
dc.subject.keywordAuthor | wearable computing | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.